Automated segmentation of kidneys and kidney tumors assists physicians to obtain accurate morphometrical details of the tumor in an efficient and reliable manner as the manual delineation process is often tedious and error-prone. The decision for kidney tumor treatment plan can be made by leveraging such important tumor's morphometrical information. Recently, deep learning approaches for semantic image segmentation have demonstrated prominent results in medical image analysis for various applications [1]}, [2]}, [3]}, [4]}. The powerful non-linear feature extraction capabilities of CNNs along with the effectiveness of the encoder-decoder architectures have made it possible to employ CNNs for challenging segmentation tasks.
